Sequential random k-nearest neighbor feature selection for high-dimensional data

Feature selection based on an ensemble classifier has been recognized as a crucial technique for modeling high-dimensional data. Feature selection based on the random forests model, which is constructed by aggregating multiple decision tree classifiers, has been widely used. However, a lack of stability and balance in decision trees decreases the robustness of random forests. This limitation motivated us to propose a feature selection method based on newly designed nearest-neighbor ensemble classifiers. The proposed method finds significant features by using an iterative procedure. We performed experiments with 20 datasets of microarray gene expressions to examine the property of the proposed method and compared it with random forests. The results demonstrated the effectiveness and robustness of the proposed method, especially when the number of features exceeds the number of observations.